Stereoscopic (e.g., three-dimensional) converters are generally designed to take traditional two-dimensional content and convert it into a polarized anaglyph image, or essentially two images of slightly different perspective and light level superimposed on each other. In the process of creating a stereoscopic perspective image out of a two-dimensional image, objects or portions of objects within the image may be repositioned along the horizontal, or X axis. By way of example, an object within an image can be “defined” by drawing around or outlining an area of pixels within the image. Once such an object has been defined, an appropriate depth can be “assigned” to that object in the resulting stereoscopic image by horizontally shifting the object in the alternate perspective view. To this end, depth placement algorithms or the like can be assigned to objects for the purpose of placing the objects at their appropriate depth locations.
Once portrait images appear in photos or films, object(s) (e.g., figure(s)) in the foreground, such as one or more individuals, rather than the background, generally become the focused area. For instance, conversion software generally analyzes shapes and colors, determines the objects that are present in the foreground and background, and then creates a map of the images to create two slightly different versions giving the parallax image our eyes need to receive to experience stereoscopic viewing. For two-dimensional to stereoscopic conversion, a closer depth value is assigned to the figures in the foreground. However, in existing processes, such as k-means segmentation, it is often difficult to precisely analyze and separate the figures from the background, so incorrect segmentations may introduce notable defects in two-dimensional to stereoscopic conversion processing. Stated differently, it is often difficult to assign proper depth values to each segment, which may result in a less than satisfactory stereoscopic experience.